FAST FULLY 3D IMAGE RECONSTRUCTION USING PLANOGRAMS

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DESCRIPTION (Adapted from Applicant's Abstract): The goal of this proposal is to implement and comparatively evaluate a reconstruction algorithm based on a new data acquisition format called a "planogram" that is applicable to PET scanners with flat detector surfaces. The planogram algorithm uses a data acquisition format that is intrinsic to dual-head SPECT scanners operated in coincidence mode. These scanners have received considerable attention as cost-effective alternatives to PET scanners for whole-body oncology and are in use at an ever increasing number of hospitals. There is thus a large base of existing scanners that can take advantage of fast and accurate reconstruction algorithm. Current reconstruction methods are unable to generate images from coincident-mode SPECT scanner data in a feasible time without trade-offs in accuracy or propagation of statistical noise. The applicants described having derived mathematical relationships for the planogram format that, while conceptually complex, are computationally simple and allow the reconstruction computations to be performed entirely with fast Fourier transforms (FFTs). This will allow coincidence-mode SPECT systems, and PET scanners with a flat similar plate detector geometry, to realize their full sensitivity while still reconstructing images in a clinically feasible time. It is expected that the planogram algorithm can be optimized to reduce image reconstruction time by about two orders of magnitude, compared to current techniques, without any increase in noise propagation or loss of resolution. The specific aims of this proposal are to (i) implement and validate the planogram reconstruction algorithm and (ii) compare the planogram algorithm to standard 3D reconstruction methods in terms of reconstruction time, accuracy, and reduction o statistical noise. The long-term objective of this research is to assist in the development of cost-effective whole-body PET scanners for clinical oncology imaging, for which the image reconstruction is a critical component.